Increasing the share of multimodal journeys is becoming more and more urgent in our society in order to guarantee a high level of mobility in the long term. While car drivers are already assisted by advanced guidance and navigation facilities, continuous on-trip assistance for multimodal travelers is still in its infancy. Especially when it comes to situations of modal change, travelers get discouraged by the increased complexity and the lack of adequate information and guidance. Thus, the goal of our research over the past three years has been to integrate existing information systems and to design and implement the prototype of a digital personal travel companion for multimodal travelers. This paper discusses typical travel situations and possible barriers for people traveling on multimodal journeys. To address these challenges, functional requirements for a personal travel companion are derived from the analysis of the situations. The main sections of this paper describe our results focusing on personalized multimodal journey planning, mobile multimodal trip management, and smart-phone-based pedestrian orientation and guidance in complex public transport transfer buildings

From April to November 2017, the non-profit research organisation Salzburg Research conducted the “Digibus© 2017” trial, the first trial of a self-driving shuttle on a public road in Austria. The shuttle from the French company Navya Tech has been tested on a 1.4-km long track in the village of Koppl, which is situated approximately ten kilometres east from the City of Salzburg. The trial in Koppl was one of the first trials worldwide on public roads with mixed traffic in a rural area. The focus of this trial was on the real-world evaluation of a self-driving shuttle for bridging the first/last mile in public transport. From April to November 2017, 240 test drives with 874 passengers covering 341 test kilometres have been conducted. Results show that the technology is ready for testing, but there is still a long way to go for driverless operation, especially in mixed traffic scenarios. The work describes the trial setting, the test route, the process of deploying the shuttle, experiences collected during the trial as well as results from a passenger survey. The accompanying passenger survey with 294 participants revealed high acceptance and a good feeling of safety.

Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.

Volunteered geographic information (VGI) data-sets are characterised by heterogeneity due to influences from technical, social, environmental or economic factors. As a result, mapping progress does neither follow a spatially nor a temporally equal distribution, and thus can be hardly measured or predicted. Positively stated, heterogeneity leads to interesting VGI data-sets revealing regional peculiarities such as diverse community activities. This work proposes an approach for identifying regionally and temporally different developments with respect to mapping progress. Regional mapping progress is measured with a modified version of a previously proposed model for classifying activity stages, which has been used as foundation for a massive spatial and temporal analysis of the worldwide OpenStreetMap contributions between the years 2006 and 2013. It also allows the evaluation of rural and unpopulated areas. Results reveal that regional mapping progress heavily depends on a number of distinct influences such as geographical or legal borders, data imports, unexpected events or diverse community developments. The work highlights regions with distinct results by revealing individual mapping stories.